A Zillow API integration checklist before launch

Test the contract your users depend on

A Zillow API integration is ready when it handles realistic successes and failures, not just one populated example. Define the expected behavior for identity, missing fields, errors, timestamps and usage before testing. That turns a demo into a reviewable application contract.

Use fixture tests to exercise your code predictably, then a small live check for the deployed integration. Keep the two evidence types separate. A fixture can prove that null estimates display correctly, but it cannot prove that a particular property will have a Zestimate tomorrow.

Build a representative fixture set

Use clearly synthetic fixtures or source data you are permitted to retain. Never present made-up property values as observed results. Include the shapes your application is most likely to mishandle.

• A complete property response with a confirmed identity.
• A valid response with null estimates and empty histories.
• A rental with asking rent distinct from rent Zestimate.
• A unit-level address and a building-level result requiring review.
• An ambiguous address and a missing property.
• A partially successful batch with stable row mapping.
• A cached response whose collection time is earlier than the request.

Verify the security boundary

Run key-bearing requests on your server. Browser code, public repositories, analytics and error screenshots must not expose the key. Test a missing key and an invalid key without substituting a working secret into public examples.

If a public application accepts addresses or property links, validate the input and add an appropriate request limit. Do not let an anonymous form become an unbounded relay to a paid API. Keep full addresses and returned raw records out of broad analytics events.

Check visible behavior and data behavior

For each fixture, assert both what is stored and what is shown. An application can retain null correctly but display it as $0. It can save a property ID correctly but attach the result to the wrong input row in a table.

Exercise the retry budget

Simulate transient failures and verify the exact stopping condition. Test that authentication, quota and validation errors stop immediately. Test Retry-After, a client timeout and an interrupted batch response.

Record incomplete work as deferred or failed. A background job should not report complete simply because its retry queue emptied after a crash. Preserve enough state to reconcile the submitted rows without replaying every successful lookup.

Run a small live acceptance check

Use your own key and a few properties relevant to your use case. Check the deployed endpoint, returned identity, field semantics, collection timestamp and account usage. Record the date and scope of the check.

Avoid asserting coverage, throughput or availability from a handful of requests. Those need a different measurement plan. If a source field is absent, accept the documented missing state or change your product requirement rather than modifying the fixture to look complete.

Make the launch evidence repeatable

Save the fixture suite, integration configuration names, API documentation version and test results. Repeat relevant checks when the response contract or your transformation logic changes.

After launch, monitor failed lookups and user-reported mismatches. A new error pattern should lead to a specific fixture and a clear handling decision. Keep the checklist short enough to run on every meaningful integration change, while treating live source behavior as an ongoing operational concern.